Data Analytics

Data analytics is the process that converts raw data into actionable insights. In data-driven organizations, analytics increasingly relies on large datasets, automation, and advanced analytics to reveal otherwise invisible insights.

Data analytics improves processes, anticipates customer behavior, and supports more effective decision-making.

Data-driven enterprises use data analytics to support effective decision-making and power growth. Here’s everything you need to know about data analytics and its role in modern, innovative companies.

Why data analytics is important in the 4th Industrial Revolution

A Fourth Industrial Revolution is transforming every industry in a wave of exponential change driven by data analytics. This new way of surfacing insights from vast oceans of data powers four types of data analytics:

  • Descriptive analytics documents events, trends, and patterns in the past.
  • Diagnostic analytics evaluates how and why those events occurred.
  • Predictive analytics forecasts future events, trends, and patterns.
  • Prescriptive analytics recommends future actions.

Companies that adopt modern approaches to leveraging their data better position themselves for this future by fostering new ways of doing business, including:

Data-driven culture and decision making

Today’s companies can’t afford to limit data to a small, centralized group of highly-specialized statisticians. Business moves too fast.

Initiatives that democratize data access and analysis encourage everyone to use properly analyzed data, leading to better, more responsive business decisions.

Innovation work

Data analytics can advance materials science or create new pharmaceuticals. Identifying urban traffic patterns can lead to opportunities in property development.

Data-driven cultures are inherently more innovative because they become better at anticipating market opportunities and developing products with superior product-market fits.

Process optimization

Every business process generates a wealth of data, creating opportunities to improve performance. But many companies let their data troves go to waste.

Data analytics tools let decision-makers evaluate and improve business processes. Automated systems can perform these optimizations in real-time.

Advanced analytics and data science

Data analytics and data science provide the foundation for new techniques in artificial intelligence and machine learning. These advanced analytics approaches require training datasets that are too difficult to assemble manually. Analytics lets data scientists evaluate a sample of larger datasets that inform the creation of new AI and ML products.


Companies can collect more data about their customers than ever before. Such vast quantities of information, from website visits to demographic data, let businesses create personalized experiences with every customer interaction.

Data-driven personalization closes sales and improves customer experiences. No matter where customers engage with the company on their journey, data analytics ensures they receive the specific information they need, when they need it.

4 Important considerations in the future of data analytics

Business analytics and its role in the enterprise are still evolving, shaped by the very trends they help generate. As a result, data practitioners must consider several issues as they adopt data analytics tools.

1. Data privacy and security

The never-ending cascade of security breaches reinforces public concerns over how companies handle their personal information. Governments worldwide took notice, creating data privacy and security regulations that assign responsibility to companies that collect, store, or process personally identifiable information.

Minimizing data collection and duplication can go a long way to regulatory compliance. Just as important is having governance policies that impose need-to-know access to data without constraining the company’s data-driven culture.

2. Data quality and reliability

Effective decision-making depends on accurate metrics and insightful data analysis, which relies upon high-quality data. Many companies struggle with data quality due to:

  • Excessive data duplication,
  • Impenetrable data silos,
  • Aging and obsolete data,
  • Inconsistently formatted data,
  • Poorly cataloged data, and
  • Dark data.

Solving the problem of data quality and reliability starts with governance. This company-wide framework assigns individual responsibilities and defines how the organization stores and shares data.

At many enterprises, information architectures are patchworks of legacy, on-premises, and cloud systems. A data analytics platform that can tie these disparate data sources together without another round of duplication is the most effective way to bring order out of chaos.

3. Ethical implications

This issue may pull data engineers and scientists out of their comfort zone. However, they are in the best position to address the ethical implications of data analytics.

A company’s use of data, from credit card information to medical records, can do a lot of good for the business and its customers. That may mean recommending the perfect restaurant for date night or diagnosing a disease in its early stages.

That same information applied without ethical guardrails can do significant damage. For example, biases within the raw data can lead to discriminatory business practices.

Organizations and data practitioners proactively consider the implications of their data usage when they adopt responsible data analytics practices. This do-no-harm approach protects stakeholders and prevents the financial and reputational damage unethical behavior causes.

4. Future trends: AI, IoT, automation

The technological advances powered by data analytics are fast-moving and disruptive. Harnessing emerging types of data analytics will create growth opportunities.

Artificial intelligence and machine learning are replacing data mining in big data analytics. In addition, natural language processing can streamline personalized marketing campaigns by automating customer interactions.

Automation also helps data-driven business cultures scale with the rising data tide. Skilled data engineers are in short supply, so helping your teams do more eases the recruiting challenges.

Internet of Things (IoT) devices contribute to a new stream of real-time data. IoT trackers simplify supply chain management, while networks of IoT environmental sensors make companies more energy efficient.

Data analytics use cases

As a leader in data lake analytics, Starburst has a front-row seat to how data analytics drives informed, insightful decision-making across many industries. Here are three use cases from Starburst customers.

Healthcare data analytics example

Healthcare providers and researchers at more than 10,000 organizations in the United Kingdom rely on EMIS to get the most current and accurate data about patient health and medical practices. At the same time, EMIS has an ethical and regulatory duty to protect patient privacy.

EMIS chose Starburst Enterprise to power its data lake analytics solution. EMIS users can combine multiple data sources reveal clinical and operational insights they would have missed. These capabilities will improve patient outcomes and reduce the cost of care as providers diagnose patients faster and develop more personalized treatments.

Finance data analytics example

Bank Hapoalim, an international bank based in Israel, replaced its traditional relational data warehouse with a data lake architecture. However, long data pipelines and ongoing query issues limited how broadly the bank could adopt data-driven decision-making.

Data lake analytics improved processes throughout the bank. For instance, gathering a suitable sample of users to test bank applications was once a laborious, time-consuming process. Data analytics can quickly identify users that fit a large matrix of parameters, greatly improving software quality control.

Financial firms adopting data lake analytics improve other mission-critical processes such as fraud detection, risk assessment, investment analysis, and portfolio optimization.

Customer engagement data analytics example

Omnichannel retailers must understand how customers engage with physical and online storefronts, post-sales support teams, and other services. Yet engagement goes beyond knowing which ads to display on the website. Understanding customer data lets retailers optimize inventories through demand forecasting.

Aerospike uses the Starburst query engine to empower retailers and other business customers with its real-time data platform. Data analysts can run SQL queries across clickstreams, point of sale, inventory, and historical data sources without moving data to intermediate sources. Helping customers answer their questions faster has improved Aerospike’s own customer satisfaction scores.

Key steps in the data analytics process: Legacy vs. Starburst

Legacy approaches to data analytics often leave companies with well-intentioned but unproductive data strategies. Starburst’s modern data lake analytics platform helps enterprises future-proof their data analytics processes.

1. Data collection and preparation

Companies that move and copy data within their legacy architectures face rising storage costs, the burden of pipeline maintenance, and significant security risks.

Starburst creates an abstraction layer that leaves data at its source, collecting and preparing data when the query runs.

Decoupling storage and compute minimizes duplication and reduces the need for data pipelines. As a result, companies spend less on storage infrastructure and leverage scalable compute resources.

2. Data analysis, on-premise and in the cloud

Over time, large enterprises accrete on-premises and cloud storage systems, increasing the challenge of conducting data analytics across fragmented architectures.

Starburst provides connectors for over fifty enterprise data sources to support cross-cloud and hybrid-cloud federation around the modern data lake. No matter where data lies, our data lake analytics platform seamlessly and affordably democratizes data access.

3. Sharing trustworthy data analytics and results

Modern data analytics depends on trust. Executives must trust that the data is accurate, current, and available when they need it. Legacy analytics takes time that slows speed-to-insight and erodes executive trust.

Starburst’s query engine provides a single source of access to federated data sources. Using SQL, business intelligence analysts can funnel data from across the company into dashboards and data visualization tools executives can trust.

The importance of data analytics: Your data architecture, our platform

Explore Starburst’s data lake analytics platform further by running it on your data lake. Investigate your data sources and run federated SQL queries — all from a single platform.

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